Leveraging environmental drivers to predict vector-borne disease transmission

利用环境驱动因素预测媒介传播疾病的传播

• 批准号: 10646945
• 负责人: Erin Mordecai
• 金额: $ 7.77万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10646945
• 关键词: Behavior; Climate; Communicable Diseases; Complex; Data; Data Sources; Dengue; Disease; Disease model; Ecology; Ecosystem; Environment; Epidemic; Etiology; Future; Geography; Global Change; Goals; Habitats; Human; Immunity; Malaria; Mathematics; Modeling; National Institute of General Medical Sciences; Pattern; Pharmaceutical Preparations; Predisposition; Public Health; Research; Series; System; Techniques; Temperature; Testing; Time; Vaccines; Vector-transmitted infectious disease; Work; ZIKA; disease transmission; disorder risk; econometrics; improved; land use; predictive tools; prevent; prospective; remote sensing; response; statistics; theories; tool; transmission process; vector; vector competence; vector control; vector transmission; vector-borne pathogen

Erin Mordecai NIGMS R35 ESI MIRA Summary Leveraging environmental drivers to predict vector-borne disease transmission Vector-borne diseases are an increasingly urgent public health crisis worldwide. Traditional biomedical approaches such as vaccines and drugs alone will not sustainably control vector-borne diseases or prevent future emergence. More proactive, ecological approaches that discover and disrupt the environmental drivers of vector transmission are critical for understanding and sustainably controlling disease epidemics. Predicting infectious disease dynamics from ecological drivers like climate and land use is appealing because these drivers are readily observable and often predictable, and their impacts on disease transmission are supported by mechanistic hypotheses. However, vector-borne diseases, like other ecological systems, are nonlinear, complex, and dynamic, making prediction challenging in a stochastic and changing world. My research uses brings in techniques from quantitative ecology, statistics, mathematics, econometrics, and geography as well as newly available data sources to understand and predict vector-borne disease dynamics in response to global change. Our preliminary work has shown that climate and land use are powerful predictors of geographical and seasonal patterns of disease transmission. I now propose to extend this work to understand disease dynamics using cutting edge quantitative techniques and time series data. Specifically, we will investigate how climate, habitat, behavior, and immunity interact to determine disease dynamics over space and time for malaria, Zika, dengue, and other vector-borne pathogens, building a portfolio of evidence and predictive tools from multiple complementary quantitative approaches. These include fitting increasingly sophisticated dynamic models to time series data, applying empirical dynamic modeling to infer, rather than assume, mechanistic relationships with ecological drivers, and applying econometric panel analysis to remotely sensed and geographic data to evaluate evidence for bidirectional causation between disease and human land use activities. Recent decades have witnessed both unprecedented expansions in both vector-borne disease and technological and computational capacity. In response, vector-borne disease modeling research is rapidly accelerating, with the goal of improving prospective prediction and thereby opening opportunities for proactive control. By developing and testing new theory, this project will finally allow us to leverage environmental drivers of vector-borne disease to understand the mechanisms underlying complex disease dynamics, and to predict future disease risk in changing environments.

Erin Mordecai Nigms R35 Esi Mira 概括 利用环境驱动力预测媒介传播 媒介传播的疾病是全球越来越紧迫的公共卫生危机。传统生物医学 仅疫苗和药物等方法将无法可持续控制媒介传播疾病或阻止 未来的出现。更积极的生态方法，发现并破坏环境 向量传播的驱动因素对于理解和可持续控制疾病流行至关重要。 从气候和土地使用等生态驱动因素中预测传染病动态很有吸引力，因为 这些驱动因素很容易观察到，通常是可以预见的，它们对疾病传播的影响是 由机械假设支持。但是，与其他生态系统一样，向量传播疾病是 非线性，复杂和动态，使预测在随机和不断变化的世界中具有挑战性。我的 研究使用定量生态学，统计，数学，计量经济学和 地理以及新可用的数据源，以理解和预测媒介传播疾病动态 响应全球变化。我们的初步工作表明，气候和土地利用是强大的 疾病传播的地理和季节性模式的预测指标。我现在建议将这项工作扩展到 使用尖端定量技术和时间序列数据了解疾病动态。具体来说，我们 将研究气候，栖息地，行为和免疫与确定疾病动态的相互作用 疟疾，Zika，登革热和其他媒介传播病原体的空间和时间，建立了大量证据 以及来自多种互补定量方法的预测工具。这些包括越来越安装 精致的动态模型到时间序列数据，将经验动态建模应用于推断而不是 假设，机械关系与生态驱动因素，并应用计量经济学面板分析以远程 感知和地理数据以评估疾病与人类土地之间双向因果关系的证据 使用活动。 最近的几十年见证了媒介传播疾病和 技术和计算能力。作为回应，媒介传播的疾病建模研究迅速 加速，目的是改善潜在的预测，从而开放积极主动的机会 控制。通过开发和测试新理论，该项目最终将使我们能够利用环境 媒介传播疾病的驱动因素了解复杂疾病动态的机制， 并在不断变化的环境中预测未来的疾病风险。